Inverter-Based Diesel Generator Emulator for the Study of Frequency Variations in a Laboratory-Scale Autonomous Power System
Bibliographic record
Abstract
This paper presents the modeling, simulation and practical implementation of an inverter-based diesel generator emulator. The main purpose of this emulator is for the study of frequency variations in diesel-based autonomous power systems in a laboratory environment where the operation of a real diesel generator is not possible. The emulator basically consists in a voltage source inverter with a second order output filter which voltage references are given by the model of the diesel generator. The control of the emulator is based on the digital signal processor TMS320F2812, where the mathematical models of the diesel generator and the control of the inverter are computed in real-time. Parameters for the model were obtained from commercially available components. Experimental results for different values of speed droop showed that the emulator achieves a satisfactory performance in the transient and stationary response. For the stationary response, the measured frequency deviates from theoretical values with a mean absolute error of: 0.06 Hz for 0% droop, 0.037 Hz for 3% droop, and 0.087 Hz for 5% droop. For the transient response, the measured frequency nadir deviates from simulations in: 0.05 Hz for 0% droop, 0.02 Hz for 3% droop, and 0.1 Hz for 5% droop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".